A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction

2021/05/05 by Ziyu Wang, Jie Yang, Wang, Ziyu +3
Computer Science · Medicine · Neuroscience · #Blind Source Separation Techniques #EEG and Brain-Computer Interfaces #Epilepsy research and treatment #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2105.02823

openalex publication_date 2021/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN) is proposed to analyze time, frequency, and channel information of electroencephalography (EEG) signals. The model uses three-dimensional (3D) kernels to facilitate the feature extraction over the three dimensions. The application of multiscale dilated convolution enables the 3D kernel to have more flexible receptive fields. The proposed CNN model is evaluated with the CHB-MIT EEG database, the experimental results indicate that our model outperforms the existing state-of-the-art, achieves 80.5% accuracy, 85.8% sensitivity and 75.1% specificity.

Citations

• PhysioBank, PhysioToolkit, and PhysioNet
• Multi-Scale Context Aggregation by Dilated Convolutions

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